Evidence map›Paper›PMID 36650474›Full record

ArticleBMC infectious diseases2023

Real-time analysis of hospital length of stay in a mixed SARS-CoV-2 Omicron and Delta epidemic in New South Wales, Australia.

Ruarai J Tobin, James G Wood, Duleepa Jayasundara, Grant Sara, Camelia R Walker, Genevieve E Martin, James M McCaw, Freya M Shearer, David J Price

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
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  12. Modelling COVID-19 transmission using IDSIM, an epidemiological-modelling desktop app with multi-level immunization capabilities.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ruarai J TobinMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia. ruarai.tobin@unimelb.edu.au.
James G WoodSchool of Population Health, University of New South Wales, Sydney, Australia.
Duleepa JayasundaraSystem Information and Analytics Branch, New South Wales Ministry of Health, Sydney, Australia.
Grant SaraSystem Information and Analytics Branch, New South Wales Ministry of Health, Sydney, Australia.
Camelia R WalkerSchool of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.
Genevieve E MartinDepartment of Infectious Diseases, Melbourne Medical School, The University of Melbourne, Melbourne, Australia.
James M McCawMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
Freya M ShearerMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
David J PriceMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia. david.price1@unimelb.edu.au.

Funding

National Health and Medical Research Council of Australia Investigator Grant Scheme Emerging Leader Fellowship, 2021/GNT2010051
6 · The paper itself

Abstract

backgroundThe distribution of the duration that clinical cases of COVID-19 occupy hospital beds (the 'length of stay') is a key factor in determining how incident caseloads translate into health system burden. Robust estimation of length of stay in real-time requires the use of survival methods that can account for right-censoring induced by yet unobserved events in patient progression (e.g. discharge, death). In this study, we estimate in real-time the length of stay distributions of hospitalised COVID-19 cases in New South Wales, Australia, comparing estimates between a period where Delta was the dominant variant and a subsequent period where Omicron was dominant.

methodsUsing data on the hospital stays of 19,574 individuals who tested positive to COVID-19 prior to admission, we performed a competing-risk survival analysis of COVID-19 clinical progression.

resultsDuring the mixed Omicron-Delta epidemic, we found that the mean length of stay for individuals who were discharged directly from ward without an ICU stay was, for age groups 0-39, 40-69 and 70 +, respectively, 2.16 (95% CI: 2.12-2.21), 3.93 (95% CI: 3.78-4.07) and 7.61 days (95% CI: 7.31-8.01), compared to 3.60 (95% CI: 3.48-3.81), 5.78 (95% CI: 5.59-5.99) and 12.31 days (95% CI: 11.75-12.95) across the preceding Delta epidemic (1 July 2021-15 December 2021). We also considered data on the stays of individuals within the Hunter New England Local Health District, where it was reported that Omicron was the only circulating variant, and found mean ward-to-discharge length of stays of 2.05 (95% CI: 1.80-2.30), 2.92 (95% CI: 2.50-3.67) and 6.02 days (95% CI: 4.91-7.01) for the same age groups.

conclusionsHospital length of stay was substantially reduced across all clinical pathways during a mixed Omicron-Delta epidemic compared to a prior Delta epidemic, contributing to a lessened health system burden despite a greatly increased infection burden. Our results demonstrate the utility of survival analysis in producing real-time estimates of hospital length of stay for assisting in situational assessment and planning of the COVID-19 response.

Indexed as

COVID-19SARS-CoV-2AustraliaHospitalsHumansNew South WalesCOVID-19Length of stayMulti-state modelSurvival analysis

Identifiers

PMID36650474
PMCPMC9844941

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.